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Homelessness has a major impact on the educational and health trajectories of children. Youth with disabilities may be especially vulnerable to experiencing homelessness, but little epidemiological work has been done to characterize dual disparity. Our goal was to describe the relationship between homelessness and disability among students (age 3-21) receiving public education in Massachusetts in 2018-2019. We evaluated the proportion of students with and without disabilities experiencing homelessness by county and school district.We used publicly available data from the United States and Massachusetts Department's of Education. These data used the McKinney Vento Homelessness Assistance Act definition of homelessness which is a lack of fixed, adequate, and regular housing, and disability was determined by the presence of an individualized education program or individualized family service plan. We calculated percentages of students with and without disabilities experiencing homelessness at the state, county, and district level and calculated and mapped risk of homelessness comparing students and without disability. We also determined the occurrence of disability among those experiencing homelessness.In Massachusetts public schools, 3.5% of students with disabilities experienced homelessness compared with 2.4% of students without disabilities (relative risk 1.50, 95% CI: 1.47, 1.53). A greater proportion of students with disabilities experienced homelessness compared with students without disabilities in all counties. In sum, 24.8% of students experiencing homelessness had a reported disability.In Massachusetts public schools, a greater proportion of students with disabilities experience homelessness compared with students without disabilities, and disability is common among students experiencing homelessness. We hypothesize potential mechanisms, such as the financial cost of disability, that may lead to this finding. Findings support the need for additional funding and interventions for school districts and communities to better serve vulnerable students with disabilities experiencing homelessness.

OBJECTIVE To quantify students with disabilities experiencing homelessness in the Northeastern and Mid-Atlantic US state and district public schools and compare them with those without disabilities. METHODS Data were compiled from state departments of education and federal homelessness data and were merged by using the Local Education Agency identifier. We calculated the proportion of students with and without disabilities experiencing homelessness and corresponding relative risk 95% confidence intervals. We examined changes in homelessness in Massachusetts counties compared with the 2018 to 2019 school year. RESULTS Across the 7 states and Washington, DC, 4.7% of students with disabilities experienced homelessness, 58% greater than the percentage of students without disabilities (95% confidence interval 1.57–1.59). The highest proportion of students with disabilities experiencing homelessness was in Washington, DC, and New York, with the lowest proportion in Connecticut. There was little change comparing 2018 to 2019 with 2019 to 2020 statistics in Massachusetts. CONCLUSIONS Quantifying students with disabilities experiencing homelessness provides policymakers with valuable information to be able to act to better support these students. Variations by state/district and time highlight the need for continued data collection and aggregation.

Background Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. Methods Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time. Results Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12–1.13]) in early spring, IRR = 1.01 [95%CI: 1.00–1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26–1.31] in spring, IRR = 1.07 [95%CI: 1.05–1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27–1.33] in spring, IRR = 1.20 [95%CI: 1.17–1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18–1.21] in spring, IRR = 1.14 [95%CI: 1.13–1.15] in fall). Conclusions Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

Heat poses an urgent threat to public health in cities, as the urban heat island (UHI) effect can amplify exposures, contributing to high heat-related mortality and morbidity. Urban trees have the potential to mitigate by providing substantial cooling, as well as co-benefits such as reductions in energy consumption. The City of Boston has attempted to expand its urban canopy, yet maintenance costs and high tree mortality have hindered successful canopy expansion. Here, we present an interactive web application called “Right Place, Right Tree - Boston” that aims to support informed decision-making for planting new trees. To highlight priority regions for canopy expansion, we developed a Boston-specific Heat Vulnerability Index (HVI) and present this alongside maps of summer temperatures. We also provide information about tree pests and diseases, suitability of species for various conditions, land ownership, maintenance tips, and alternatives to tree planting.

Heat poses an urgent threat to public health in cities, as the urban heat island (UHI) effect can amplify exposures, contributing to high heat-related mortality and morbidity. Urban trees have the potential to mitigate heat by providing substantial cooling, as well as co-benefits such as reductions in energy consumption. The City of Boston has attempted to expand its urban canopy, yet maintenance costs and high tree mortality have hindered successful canopy expansion. Here, we present an interactive web application called Right Place, Right Tree-Boston that aims to support informed decision-making for planting new trees. To highlight priority regions for canopy expansion, we developed a Boston-specific Heat Vulnerability Index (HVI) and present this alongside maps of summer daytime land surface temperatures. We also provide information about tree pests and diseases, suitability of species for various conditions, land ownership, maintenance tips, and alternatives to tree planting. This web application is designed to support decision-making at multiple spatial scales, to assist city officials as well as residents who are interested in expanding or maintaining Boston's urban forest.